Overview

Dataset statistics

Number of variables13
Number of observations3897
Missing cells0
Missing cells (%)0.0%
Duplicate rows410
Duplicate rows (%)10.5%
Total size in memory395.9 KiB
Average record size in memory104.0 B

Variable types

Numeric12
Categorical1

Alerts

Dataset has 410 (10.5%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with wine_typeHigh correlation
volatile acidity is highly overall correlated with wine_typeHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxide and 1 other fieldsHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 2 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
wine_type is highly overall correlated with fixed acidity and 4 other fieldsHigh correlation
citric acid has 93 (2.4%) zerosZeros

Reproduction

Analysis started2023-10-28 11:17:18.343561
Analysis finished2023-10-28 11:17:56.688096
Duration38.34 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2276751
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:17:56.933831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.9
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.3317322
Coefficient of variation (CV)0.18425458
Kurtosis4.9110989
Mean7.2276751
Median Absolute Deviation (MAD)0.6
Skewness1.7471779
Sum28166.25
Variance1.7735107
MonotonicityNot monotonic
2023-10-28T11:17:57.297499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 217
 
5.6%
6.6 214
 
5.5%
6.4 180
 
4.6%
7.2 164
 
4.2%
7 161
 
4.1%
6.9 160
 
4.1%
6.7 159
 
4.1%
7.1 155
 
4.0%
7.3 147
 
3.8%
7.4 145
 
3.7%
Other values (90) 2195
56.3%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
4.2 1
 
< 0.1%
4.4 2
 
0.1%
4.5 1
 
< 0.1%
4.7 4
 
0.1%
4.8 5
 
0.1%
4.9 3
 
0.1%
5 20
0.5%
5.1 19
0.5%
5.2 23
0.6%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 1
< 0.1%
15.5 1
< 0.1%
15 1
< 0.1%
14.3 1
< 0.1%
13.8 1
< 0.1%
13.7 2
0.1%
13.5 1
< 0.1%
13.4 1
< 0.1%
13.3 2
0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34235694
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:17:57.624183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.3
Q30.41
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1656422
Coefficient of variation (CV)0.48382897
Kurtosis3.4078917
Mean0.34235694
Median Absolute Deviation (MAD)0.08
Skewness1.5653356
Sum1334.165
Variance0.02743734
MonotonicityNot monotonic
2023-10-28T11:17:57.924047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 177
 
4.5%
0.24 162
 
4.2%
0.26 156
 
4.0%
0.27 150
 
3.8%
0.22 146
 
3.7%
0.32 133
 
3.4%
0.3 131
 
3.4%
0.23 128
 
3.3%
0.25 126
 
3.2%
0.2 118
 
3.0%
Other values (161) 2470
63.4%
ValueCountFrequency (%)
0.08 2
 
0.1%
0.1 3
 
0.1%
0.105 5
 
0.1%
0.11 10
 
0.3%
0.115 2
 
0.1%
0.12 18
0.5%
0.125 1
 
< 0.1%
0.13 25
0.6%
0.135 1
 
< 0.1%
0.14 41
1.1%
ValueCountFrequency (%)
1.58 1
 
< 0.1%
1.33 2
0.1%
1.24 1
 
< 0.1%
1.18 1
 
< 0.1%
1.13 1
 
< 0.1%
1.1 1
 
< 0.1%
1.09 1
 
< 0.1%
1.07 1
 
< 0.1%
1.04 3
0.1%
1.025 1
 
< 0.1%

citric acid
Real number (ℝ)

ZEROS 

Distinct85
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32001026
Minimum0
Maximum1
Zeros93
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:17:58.261756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.4
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.14623562
Coefficient of variation (CV)0.45697165
Kurtosis1.1568587
Mean0.32001026
Median Absolute Deviation (MAD)0.07
Skewness0.34229552
Sum1247.08
Variance0.021384856
MonotonicityNot monotonic
2023-10-28T11:17:58.639054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 195
 
5.0%
0.28 184
 
4.7%
0.49 176
 
4.5%
0.32 169
 
4.3%
0.34 162
 
4.2%
0.26 152
 
3.9%
0.29 147
 
3.8%
0.24 133
 
3.4%
0.31 133
 
3.4%
0.27 127
 
3.3%
Other values (75) 2319
59.5%
ValueCountFrequency (%)
0 93
2.4%
0.01 17
 
0.4%
0.02 39
1.0%
0.03 16
 
0.4%
0.04 26
 
0.7%
0.05 16
 
0.4%
0.06 22
 
0.6%
0.07 27
 
0.7%
0.08 18
 
0.5%
0.09 27
 
0.7%
ValueCountFrequency (%)
1 5
0.1%
0.91 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 1
 
< 0.1%
0.81 2
 
0.1%
0.8 2
 
0.1%
0.79 3
0.1%
0.78 1
 
< 0.1%
0.76 2
 
0.1%
0.75 1
 
< 0.1%

residual sugar
Real number (ℝ)

HIGH CORRELATION 

Distinct280
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3817937
Minimum0.7
Maximum26.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:17:58.994387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.2
Q11.8
median3
Q38
95-th percentile15
Maximum26.05
Range25.35
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation4.6489006
Coefficient of variation (CV)0.86381992
Kurtosis0.43803368
Mean5.3817937
Median Absolute Deviation (MAD)1.7
Skewness1.166878
Sum20972.85
Variance21.612277
MonotonicityNot monotonic
2023-10-28T11:17:59.374548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 146
 
3.7%
1.8 144
 
3.7%
1.4 133
 
3.4%
1.6 122
 
3.1%
1.2 118
 
3.0%
2.2 116
 
3.0%
1.7 111
 
2.8%
1.5 107
 
2.7%
2.1 99
 
2.5%
1.9 95
 
2.4%
Other values (270) 2706
69.4%
ValueCountFrequency (%)
0.7 3
 
0.1%
0.8 17
 
0.4%
0.9 29
 
0.7%
0.95 3
 
0.1%
1 46
 
1.2%
1.05 1
 
< 0.1%
1.1 92
2.4%
1.15 2
 
0.1%
1.2 118
3.0%
1.25 2
 
0.1%
ValueCountFrequency (%)
26.05 2
0.1%
22 2
0.1%
20.8 2
0.1%
20.3 1
 
< 0.1%
20.15 1
 
< 0.1%
19.95 3
0.1%
19.9 1
 
< 0.1%
19.8 1
 
< 0.1%
19.6 1
 
< 0.1%
19.5 2
0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct181
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056255068
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:17:59.683188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.066
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.028

Descriptive statistics

Standard deviation0.035657042
Coefficient of variation (CV)0.63384586
Kurtosis56.34578
Mean0.056255068
Median Absolute Deviation (MAD)0.011
Skewness5.6651288
Sum219.226
Variance0.0012714247
MonotonicityNot monotonic
2023-10-28T11:17:59.988950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 134
 
3.4%
0.042 116
 
3.0%
0.034 114
 
2.9%
0.048 113
 
2.9%
0.036 112
 
2.9%
0.05 112
 
2.9%
0.045 112
 
2.9%
0.04 109
 
2.8%
0.038 105
 
2.7%
0.047 101
 
2.6%
Other values (171) 2769
71.1%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 1
 
< 0.1%
0.014 3
 
0.1%
0.015 3
 
0.1%
0.016 4
0.1%
0.017 2
 
0.1%
0.018 6
0.2%
0.019 3
 
0.1%
0.02 8
0.2%
0.021 9
0.2%
ValueCountFrequency (%)
0.611 1
< 0.1%
0.61 1
< 0.1%
0.422 1
< 0.1%
0.415 1
< 0.1%
0.414 2
0.1%
0.413 1
< 0.1%
0.403 1
< 0.1%
0.387 1
< 0.1%
0.368 1
< 0.1%
0.36 1
< 0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.56582
Minimum2
Maximum146.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:18:00.260883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum146.5
Range144.5
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.828001
Coefficient of variation (CV)0.58326592
Kurtosis1.7436102
Mean30.56582
Median Absolute Deviation (MAD)12
Skewness0.89308059
Sum119115
Variance317.83762
MonotonicityNot monotonic
2023-10-28T11:18:00.552800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 107
 
2.7%
6 104
 
2.7%
15 103
 
2.6%
31 95
 
2.4%
24 94
 
2.4%
27 90
 
2.3%
26 89
 
2.3%
34 89
 
2.3%
35 87
 
2.2%
28 85
 
2.2%
Other values (108) 2954
75.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 39
 
1.0%
4 30
 
0.8%
5 84
2.2%
6 104
2.7%
7 63
1.6%
8 46
1.2%
9 52
1.3%
10 83
2.1%
11 62
1.6%
ValueCountFrequency (%)
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
108 2
0.1%
105 1
< 0.1%
101 2
0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct265
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.24942
Minimum6
Maximum366.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:18:00.910384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3155
95-th percentile207
Maximum366.5
Range360.5
Interquartile range (IQR)78

Descriptive statistics

Standard deviation56.764684
Coefficient of variation (CV)0.49253769
Kurtosis-0.45827173
Mean115.24942
Median Absolute Deviation (MAD)38
Skewness-0.0024354061
Sum449127
Variance3222.2294
MonotonicityNot monotonic
2023-10-28T11:18:01.271379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 42
 
1.1%
126 38
 
1.0%
98 38
 
1.0%
125 36
 
0.9%
124 36
 
0.9%
122 36
 
0.9%
87 36
 
0.9%
119 35
 
0.9%
116 35
 
0.9%
118 35
 
0.9%
Other values (255) 3530
90.6%
ValueCountFrequency (%)
6 3
 
0.1%
7 1
 
< 0.1%
8 10
0.3%
9 11
0.3%
10 12
0.3%
11 15
0.4%
12 20
0.5%
13 20
0.5%
14 23
0.6%
15 21
0.5%
ValueCountFrequency (%)
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%
272 2
0.1%
260 1
< 0.1%
256 1
< 0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct871
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99469028
Minimum0.98711
Maximum1.00315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:18:01.811647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.989924
Q10.99228
median0.9949
Q30.99694
95-th percentile0.9993
Maximum1.00315
Range0.01604
Interquartile range (IQR)0.00466

Descriptive statistics

Standard deviation0.0029469664
Coefficient of variation (CV)0.0029626975
Kurtosis-0.76983279
Mean0.99469028
Median Absolute Deviation (MAD)0.0023
Skewness-0.034972275
Sum3876.308
Variance8.6846108 × 10-6
MonotonicityNot monotonic
2023-10-28T11:18:02.105738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9976 44
 
1.1%
0.9986 41
 
1.1%
0.9972 40
 
1.0%
0.9944 39
 
1.0%
0.992 36
 
0.9%
0.9958 36
 
0.9%
0.9968 35
 
0.9%
0.9932 34
 
0.9%
0.9984 34
 
0.9%
0.998 34
 
0.9%
Other values (861) 3524
90.4%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 1
< 0.1%
0.98746 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 1
< 0.1%
0.98815 1
< 0.1%
0.98816 1
< 0.1%
ValueCountFrequency (%)
1.00315 2
0.1%
1.00295 2
0.1%
1.00289 1
< 0.1%
1.0026 2
0.1%
1.00242 2
0.1%
1.00241 1
< 0.1%
1.0022 2
0.1%
1.0021 1
< 0.1%
1.00196 1
< 0.1%
1.0018 1
< 0.1%

pH
Real number (ℝ)

Distinct103
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2170131
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:18:02.408821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile2.96
Q13.1
median3.2
Q33.32
95-th percentile3.5
Maximum4.01
Range1.27
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.16102424
Coefficient of variation (CV)0.05005396
Kurtosis0.31248253
Mean3.2170131
Median Absolute Deviation (MAD)0.11
Skewness0.37401536
Sum12536.7
Variance0.025928807
MonotonicityNot monotonic
2023-10-28T11:18:02.767716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 118
 
3.0%
3.14 113
 
2.9%
3.22 109
 
2.8%
3.2 108
 
2.8%
3.19 108
 
2.8%
3.15 101
 
2.6%
3.08 98
 
2.5%
3.3 97
 
2.5%
3.1 97
 
2.5%
3.24 96
 
2.5%
Other values (93) 2852
73.2%
ValueCountFrequency (%)
2.74 2
 
0.1%
2.79 1
 
< 0.1%
2.8 2
 
0.1%
2.82 1
 
< 0.1%
2.83 3
 
0.1%
2.84 1
 
< 0.1%
2.85 7
0.2%
2.86 3
 
0.1%
2.87 7
0.2%
2.88 8
0.2%
ValueCountFrequency (%)
4.01 1
 
< 0.1%
3.9 1
 
< 0.1%
3.85 1
 
< 0.1%
3.82 1
 
< 0.1%
3.81 1
 
< 0.1%
3.8 1
 
< 0.1%
3.78 1
 
< 0.1%
3.76 2
0.1%
3.75 3
0.1%
3.74 1
 
< 0.1%

sulphates
Real number (ℝ)

Distinct103
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53222222
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:18:03.124677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.8
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1504541
Coefficient of variation (CV)0.28269038
Kurtosis8.4198238
Mean0.53222222
Median Absolute Deviation (MAD)0.08
Skewness1.8115237
Sum2074.07
Variance0.022636436
MonotonicityNot monotonic
2023-10-28T11:18:03.473398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 161
 
4.1%
0.54 140
 
3.6%
0.46 138
 
3.5%
0.44 135
 
3.5%
0.48 131
 
3.4%
0.38 127
 
3.3%
0.52 120
 
3.1%
0.53 119
 
3.1%
0.49 118
 
3.0%
0.45 116
 
3.0%
Other values (93) 2592
66.5%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 3
 
0.1%
0.26 3
 
0.1%
0.27 6
 
0.2%
0.28 6
 
0.2%
0.29 13
0.3%
0.3 24
0.6%
0.31 18
0.5%
0.32 29
0.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%
1.28 1
 
< 0.1%
1.26 1
 
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.48646
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:18:03.782387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1985447
Coefficient of variation (CV)0.11429451
Kurtosis-0.51830535
Mean10.48646
Median Absolute Deviation (MAD)0.9
Skewness0.57474188
Sum40865.733
Variance1.4365095
MonotonicityNot monotonic
2023-10-28T11:18:04.090966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 220
 
5.6%
9.4 202
 
5.2%
9.2 167
 
4.3%
10 145
 
3.7%
9.8 134
 
3.4%
9 133
 
3.4%
11 132
 
3.4%
10.5 132
 
3.4%
10.2 124
 
3.2%
10.4 115
 
3.0%
Other values (82) 2393
61.4%
ValueCountFrequency (%)
8 2
 
0.1%
8.4 3
 
0.1%
8.5 7
 
0.2%
8.6 15
 
0.4%
8.7 58
 
1.5%
8.8 66
 
1.7%
8.9 46
 
1.2%
9 133
3.4%
9.1 98
2.5%
9.2 167
4.3%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14 7
0.2%
13.9 2
 
0.1%
13.8 1
 
< 0.1%
13.7 5
 
0.1%
13.6 8
0.2%
13.5 7
0.2%
13.4 16
0.4%
13.3 5
 
0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8196048
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.6 KiB
2023-10-28T11:18:04.386989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.879288
Coefficient of variation (CV)0.15109067
Kurtosis0.19994369
Mean5.8196048
Median Absolute Deviation (MAD)1
Skewness0.23421838
Sum22679
Variance0.77314738
MonotonicityNot monotonic
2023-10-28T11:18:04.658145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1681
43.1%
5 1294
33.2%
7 646
 
16.6%
4 134
 
3.4%
8 123
 
3.2%
3 15
 
0.4%
9 4
 
0.1%
ValueCountFrequency (%)
3 15
 
0.4%
4 134
 
3.4%
5 1294
33.2%
6 1681
43.1%
7 646
 
16.6%
8 123
 
3.2%
9 4
 
0.1%
ValueCountFrequency (%)
9 4
 
0.1%
8 123
 
3.2%
7 646
 
16.6%
6 1681
43.1%
5 1294
33.2%
4 134
 
3.4%
3 15
 
0.4%

wine_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.6 KiB
white
2923 
red
974 

Length

Max length5
Median length5
Mean length4.5001283
Min length3

Characters and Unicode

Total characters17537
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowred
3rd rowred
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 2923
75.0%
red 974
 
25.0%

Length

2023-10-28T11:18:04.973887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-28T11:18:05.222065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
white 2923
75.0%
red 974
 
25.0%

Most occurring characters

ValueCountFrequency (%)
e 3897
22.2%
w 2923
16.7%
h 2923
16.7%
i 2923
16.7%
t 2923
16.7%
r 974
 
5.6%
d 974
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17537
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3897
22.2%
w 2923
16.7%
h 2923
16.7%
i 2923
16.7%
t 2923
16.7%
r 974
 
5.6%
d 974
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 17537
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3897
22.2%
w 2923
16.7%
h 2923
16.7%
i 2923
16.7%
t 2923
16.7%
r 974
 
5.6%
d 974
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3897
22.2%
w 2923
16.7%
h 2923
16.7%
i 2923
16.7%
t 2923
16.7%
r 974
 
5.6%
d 974
 
5.6%

Interactions

2023-10-28T11:17:52.927963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:21.002247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:24.226270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:27.024140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:29.801305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:32.637687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:35.433751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:38.117527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:41.394606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:44.332687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:47.165600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:50.163363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:53.185233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:21.207228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:24.469032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:27.228878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:30.023119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:32.849491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:35.644843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:38.379800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:41.631807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:44.588999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:47.414506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:50.407820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:53.630159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:21.453474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:24.707011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:27.428217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:30.414518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:33.066959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:35.855963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:38.634929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:42.063089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:44.882242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:47.656534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:50.659571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:53.864764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:21.711078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:24.914583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:27.617319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:30.614149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:33.359015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:36.059488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:38.891607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:42.290590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:45.131950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:47.880706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:50.888593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:54.064267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:21.973291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:25.141445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:27.809849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:30.798027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:33.554384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:36.287919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:39.127072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:42.509959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:45.369418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:48.109449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:51.121677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:54.259199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:22.253014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:25.351825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:28.035749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:30.998721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:33.808910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:36.514978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:39.344538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:42.721562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:45.575363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:48.341031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:51.365937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:54.483947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:22.562194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:25.573756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:28.276471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:31.222677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:34.063360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:36.768920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:39.563088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:42.951881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:45.793675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:48.602056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:51.614920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:54.702938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:22.850985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:25.817529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:28.594751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:31.459906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:34.302487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:37.000719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:39.780310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:43.217830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:45.990292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:48.888448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:51.834560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:54.946128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:23.143802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:26.072248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:28.899821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:31.701321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:34.561001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:37.273528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:40.115991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:43.461942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:46.201633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:49.165191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:52.065431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:55.226068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:23.422717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:26.328154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:29.091274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:31.933965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:34.757008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:37.490142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:40.646066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:43.662374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:46.396939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:49.447957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:52.271455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:55.478111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:23.735612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:26.582295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:29.304609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:32.151956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:34.956530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:37.696530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:40.887010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:43.868346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:46.665014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:49.658480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:52.495453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:55.724912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:24.002544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:26.809318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:29.551557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:32.404519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:35.197465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:37.916835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:41.133536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:44.106593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:46.924123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:49.939937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-28T11:17:52.696618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-28T11:18:05.429500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitywine_type
fixed acidity1.0000.1860.284-0.0360.354-0.261-0.2390.433-0.2460.218-0.105-0.0900.514
volatile acidity0.1861.000-0.296-0.0680.414-0.377-0.3540.2570.2190.247-0.013-0.2690.654
citric acid0.284-0.2961.0000.075-0.0530.1330.1600.083-0.2860.0440.0090.1040.439
residual sugar-0.036-0.0680.0751.000-0.0350.3610.4510.523-0.224-0.132-0.335-0.0200.427
chlorides0.3540.414-0.053-0.0351.000-0.271-0.2860.5930.1710.364-0.396-0.3000.766
free sulfur dioxide-0.261-0.3770.1330.361-0.2711.0000.742-0.019-0.169-0.220-0.1810.0810.543
total sulfur dioxide-0.239-0.3540.1600.451-0.2860.7421.0000.044-0.247-0.259-0.307-0.0630.809
density0.4330.2570.0830.5230.593-0.0190.0441.0000.0220.274-0.692-0.3220.435
pH-0.2460.219-0.286-0.2240.171-0.169-0.2470.0221.0000.2600.1370.0400.345
sulphates0.2180.2470.044-0.1320.364-0.220-0.2590.2740.2601.0000.0220.0380.477
alcohol-0.105-0.0130.009-0.335-0.396-0.181-0.307-0.6920.1370.0221.0000.4610.145
quality-0.090-0.2690.104-0.020-0.3000.081-0.063-0.3220.0400.0380.4611.0000.128
wine_type0.5140.6540.4390.4270.7660.5430.8090.4350.3450.4770.1450.1281.000

Missing values

2023-10-28T11:17:56.052196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-28T11:17:56.503385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitywine_type
09.50.4200.412.30.03422.0145.00.995103.060.5211.06.0white
17.60.6650.101.50.06627.055.00.996553.390.519.35.0red
28.50.2800.351.70.0616.015.00.995243.300.7411.87.0red
36.10.2000.401.90.02832.0138.00.991403.260.7211.75.0white
46.40.2800.447.10.04849.0179.00.995283.150.489.25.0white
55.70.2700.169.00.05332.0111.00.994743.360.3710.46.0white
67.40.1600.2715.50.05025.0135.00.998402.900.438.77.0white
77.90.1800.495.20.05136.0157.00.995303.180.4810.66.0white
812.00.3700.764.20.0667.038.01.000403.220.6013.07.0red
95.60.2100.401.30.04181.0147.00.990103.220.9511.68.0white
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitywine_type
38878.90.330.321.50.04711.0200.00.995403.190.469.405.0white
38885.60.620.031.50.0806.013.00.994983.660.6210.104.0red
38897.50.240.291.10.04634.084.00.990203.040.3911.456.0white
38906.20.150.271.40.04151.0117.00.990903.280.3811.206.0white
38917.10.200.279.60.03719.0105.00.994443.040.3710.507.0white
38927.70.250.307.80.03867.0196.00.995553.100.5010.105.0white
389310.70.430.392.20.1068.032.00.998602.890.509.605.0red
389410.00.290.402.90.09810.026.01.000603.480.919.705.0red
38955.20.240.453.80.02721.0128.00.992003.550.4911.208.0white
38966.50.230.3616.30.03843.0133.00.999243.260.418.805.0white

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitywine_type# duplicates
2797.40.160.3013.70.05633.0168.00.998252.900.448.77.0white6
1576.80.180.3012.80.06219.0171.00.998083.000.529.07.0white5
1947.00.150.2814.70.05129.0149.00.997922.960.399.07.0white5
2837.40.190.3114.50.04539.0193.00.998603.100.509.26.0white5
1176.60.220.2317.30.04737.0118.00.999063.080.468.86.0white4
1386.70.160.3212.50.03518.0156.00.996662.880.369.06.0white4
1496.70.460.241.70.07718.034.00.994803.390.6010.66.0red4
2787.40.160.2715.50.05025.0135.00.998402.900.438.77.0white4
2827.40.190.3012.80.05348.5229.00.998603.140.499.17.0white4
15.00.330.161.50.04910.097.00.991703.480.4410.76.0white3